A cardioviral 2C-ATP complex structure reveals the essential role of a conserved arginine in regulation of cardioviral 2C activity.

2C is a highly conserved picornaviral non-structural protein with ATPase activity and plays a multifunctional role in the viral life cycle as a promising target for anti-picornavirus drug development. While the structure-function of enteroviral 2Cs have been well studied, cardioviral 2Cs remain largely uncharacterized. Here, an endogenous ATP molecule was identified in the crystal structure of 2C from encephalomyocarditis virus (EMCV, Cardiovirus A). The ATP is bound into the ATPase active site with a unique compact conformation. Notably, the γ-phosphate of ATP directly interacts with Arg311 (conserved in cardioviral 2Cs), and its mutation significantly inhibits the ATPase activity. Unexpectedly, this mutation remarkably promotes 2C self-oligomerization and viral replication efficiency. Molecular dynamic simulations showed that the Arg311 side chain is highly dynamic, indicating it may function as a switch between the activation state and the inhibition state of ATPase activity. A hexameric ring model of EMCV 2C full length indicated that the C-terminal helix may get close to the N-terminal amphipathic helices to form a continuous positive region for RNA binding. The RNA-binding studies of EMCV 2C revealed that the RNA length is closely associated with the RNA-binding affinities and indicated that the substrate may wrap around the outer surface of the hexamer. Our studies provide a biochemical framework to guide the characterization of EMCV 2C and the essential role of arginine in cardioviral 2C functions. IMPORTANCE: Encephalomyocarditis virus (Cardiovirus A) is the causative agent of the homonymous disease, which may induce myocarditis, encephalitis, and reproductive disorders in various mammals. 2C protein is functionally indispensable and a promising target for drug development involving broad-spectrum picornaviral inhibitors. Here, an endogenous ATP molecule with a unique conformation was discovered by a combination of protein crystallography and high-performance liquid chromatography in the encephalomyocarditis virus (EMCV) 2C structure. Biochemical and structural characterization analysis of EMCV 2C revealed the critical role of conserved Arg311 in ATPase activity and self-oligomerization of EMCV 2C. The viral replication kinetics and infectivity study suggested that the residue negatively regulated the infectivity titer and virus encapsulation efficiency of EMCV and is, therefore, crucial for 2C protein to promote viral replication. Our systemic structure-function analysis provides unique insights into the function and regulation mechanism of cardioviral 2C protein.

Allosteric regulation of Senecavirus A 3Cpro proteolytic activity by an endogenous phospholipid.

Seneca virus A (SVA) is an emerging novel picornavirus that has recently been identified as the causative agent of many cases of porcine vesicular diseases in multiple countries. In addition to cleavage of viral polyprotein, the viral 3C protease (3Cpro) plays an important role in the regulation of several physiological processes involved in cellular antiviral responses by cleaving critical cellular proteins. Through a combination of crystallography, untargeted lipidomics, and immunoblotting, we identified the association of SVA 3Cpro with an endogenous phospholipid molecule, which binds to a unique region neighboring the proteolytic site of SVA 3Cpro. Our lipid-binding assays showed that SVA 3Cpro displayed preferred binding to cardiolipin (CL), followed by phosphoinositol-4-phosphate (PI4P) and sulfatide. Importantly, we found that the proteolytic activity of SVA 3Cpro was activated in the presence of the phospholipid, and the enzymatic activity is inhibited when the phospholipid-binding capacity decreased. Interestingly, in the wild-type SVA 3Cpro-substrate peptide structure, the cleavage residue cannot form a covalent binding to the catalytic cysteine residue to form the acyl-enzyme intermediate observed in several picornaviral 3Cpro structures. We observed a decrease in infectivity titers of SVA mutants harboring mutations that impaired the lipid-binding ability of 3Cpro, indicating a positive regulation of SVA infection capacity mediated by phospholipids. Our findings reveal a mutual regulation between the proteolytic activity and phospholipid-binding capacity in SVA 3Cpro, suggesting that endogenous phospholipid may function as an allosteric activator that regulate the enzyme's proteolytic activity during infection.

MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs.

BACKGROUND: Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge. RESULTS: We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA. CONCLUSIONS: During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.

Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.

BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online platforms based on deep learning for drug-target interaction, affinity, and binding sites identification, there is currently no integrated online platforms for all three aspects. RESULTS: Our solution, the novel integrated online platform Drug-Online, has been developed to facilitate drug screening, target identification, and understanding the functions of target in a progressive manner of "interaction-affinity-binding sites". Drug-Online platform consists of three parts: the first part uses the drug-target interaction identification method MGraphDTA, based on graph neural networks (GNN) and convolutional neural networks (CNN), to identify whether there is a drug-target interaction. If an interaction is identified, the second part employs the drug-target affinity identification method MMDTA, also based on GNN and CNN, to calculate the strength of drug-target interaction, i.e., affinity. Finally, the third part identifies drug-target binding sites, i.e., pockets. The method pt-lm-gnn used in this part is also based on GNN. CONCLUSIONS: Drug-Online is a reliable online platform that integrates drug-target interaction, affinity, and binding sites identification. It is freely available via the Internet at http://39.106.7.26:8000/Drug-Online/ .

GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs.

Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.

GNN-DDAS: Drug discovery for identifying anti-schistosome small molecules based on graph neural network.

Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti-schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer-aided methods. To address this issue, we proposed GNN-DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti-schistosome (DDAS) small molecules. Initially, a multi-layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti-schistosome small molecules. Experimental results showed that GNN-DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real-world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN-DDAS. Overall, GNN-DDAS provided a promising solution for discovering new and active anti-schistosome small molecules.

MMDTA: A Multimodal Deep Model for Drug-Target Affinity with a Hybrid Fusion Strategy.

The prediction of the drug-target affinity (DTA) plays an important role in evaluating molecular druggability. Although deep learning-based models for DTA prediction have been extensively attempted, there are rare reports on multimodal models that leverage various fusion strategies to exploit heterogeneous information from multiple different modalities of drugs and targets. In this study, we proposed a multimodal deep model named MMDTA, which integrated the heterogeneous information from various modalities of drugs and targets using a hybrid fusion strategy to enhance DTA prediction. To achieve this, MMDTA first employed convolutional neural networks (CNNs) and graph convolutional networks (GCNs) to extract diverse heterogeneous information from the sequences and structures of drugs and targets. It then utilized a hybrid fusion strategy to combine and complement the extracted heterogeneous information, resulting in the fused modal information for predicting drug-target affinity through the fully connected (FC) layers. Experimental results demonstrated that MMDTA outperformed the competitive state-of-the-art deep learning models on the widely used benchmark data sets, particularly with a significantly improved key evaluation metric, Root Mean Square Error (RMSE). Furthermore, MMDTA exhibited excellent generalization and practical application performance on multiple different data sets. These findings highlighted MMDTA's accuracy and reliability in predicting the drug-target binding affinity. For researchers interested in the source data and code, they are accessible at http://github.com/dldxzx/MMDTA.

Pathophysiological impacts of 5-MeO-MiPT on zebrafish (Danio rerio) via the Gαq/11-PLCß signaling pathway.

Novel Psychoactive Substances (NPS) derived from tryptamines has been detected in aquatic environments, leading to environmental toxicology concerns. However, the specific toxicological mechanism, underlying these NPS, remains unclear. In our previous work, we used 5-Methoxy-N-isopropyl-N-methyltryptamine (5-MeO-MiPT) as the representative drug for NPS, and found that, 5-MeO-MiPT led to obvious behavioral inhibition and oxidative stress responses in zebrafishes model. In this study, Zebrafish were injected with varying concentrations of 5-MeO-MiPT for 30 days. RNA-seq, qPCR, metabolomics, and histopathological analyses were conducted to assess gene expression and tissue integrity. This study confirms that 5-MeO-MiPT substantially influences the transcription and expression of 13 selected genes, including ucp1, pet100, grik3, and grik4, mediated by the Gαq/11-PLCß signaling pathway. We elucidate the molecular mechanism that 5-MeO-MiPT can inhibit DAG-Ca2+/Pkc/Erk, Pkc/Pla2/PLCs and Ca2+/Camk Ⅱ/NMDA, while enhance Ca2+/Creb. Those secondary signaling pathways may be the mechanisms mediating 5-MeO-MiPT inhibiting normal behavior in zebrafish. These findings offer novel insights into the toxicological effects and addiction mechanisms of 5-MeO-MiPT. Moreover, it presents promising avenues for investigating other tryptamine-based NPS and offers a new direction for diagnosing and treating liver-brain pathway-related diseases.

Nontargeted metabolomic insights into the behavioral effects of 5-MeO-MiPT in zebrafish (Danio rerio).

5-Methoxy-N-methyl-N-isopropyltryptamine (5-MeO-MiPT) is a novel psychoactive substance exhibiting a tryptamine structure. Despite its increasing prevalence, the environmental impact of 5-MeO-MiPT remains unexplored. Our prior investigation revealed that 5-MeO-MiPT induced inhibited spontaneous movement and prompted anxiety-like behavior in adult zebrafish-a validated toxicological model. To elucidate this phenomenon and establish a correlation between metabolomics and behavioral changes induced by 5-MeO-MiPT, zebrafish were administered varying drug concentrations. Zebrafishes were subjected to injections of different 5-MeO-MiPT concentrations. Subsequent metabolomic analysis of endogenous metabolites affected by the drug unveiled substantial variations in metabolic levels between the control group and the drug-injected cohorts. A total of 22 distinct metabolites emerged as potential biomarkers. Further scrutiny identified seven pathways significantly influenced by 5-MeO-MiPT. A focused exploration into amino acid metabolism, lipid metabolism, and energy metabolism unveiled that the metabolic repercussions of 5-MeO-MiPT on zebrafish resulted in observable brain damage. Notably, the study identified a consequential disruption in the liver-brain pathway. The comprehensive metabolomic approach employed herein effectively discerned the impact of 5-MeO-MiPT on zebrafish metabolism. This approach also shed light on the mechanism underpinning the anxiety-like behavior observed in zebrafish post-drug injection. Specifically, our findings indicate that 5-MeO-MiPT induces brain damage, particularly within the liver-brain pathway.

Naringin Induces ROS-Stimulated G1 Cell-Cycle Arrest and Apoptosis in Nasopharyngeal Carcinoma Cells.

Naringin, a bioflavonoid compound from grapefruit or citrus, exerts anticancer activities on cervical, thyroid, colon, brain, liver, lung, thyroid, and breast cancers. The present investigation addressed exploring the anticancer effects of naringin on nasopharyngeal carcinoma (NPC) cells. Naringin exhibits a cytotoxic effect on NPC-TW 039 and NPC-TW 076 cells with IC50 372/328 and 394/307 µM for 24 or 48 h, respectively, while causing little toxicity toward normal gingival epithelial (SG) cells (>500/500 µM). We established that naringin triggered G1 arrest is achieved by suppressing cyclin D1, cyclin A, and CDK2, and upregulating p21 protein in NPC cells. Exposure of NPC cells to naringin caused a series of events leading to apoptosis including morphology change (cell shrinkage and membrane blebbing) and chromatin condensation. Annexin V and PI staining indicated that naringin treatment promotes necrosis and late apoptosis in NPC cells. DiOC6 staining showed a decline in the mitochondrial membrane potential by naringin treatment, which was followed with cytochrome c release, Apaf-1/caspase-9/-3 activation, PARP cleavage, and EndoG expression in NPC cells. Naringin upregulated proapoptotic Bax and decreased antiapoptotic Bcl-xL expression, and dysregulated Bax/Bcl-xL ratio in NPC cells. Notably, naringin enhanced death receptor-related t-Bid expression. Furthermore, an increased Ca2+ release by naringin treatment which instigated endoplasmic reticulum stress-associated apoptosis through increased IRE1, ATF-6, GRP78, GADD153, and caspase-12 expression in NPC cells. In addition, naringin triggers ROS production, and inhibition of naringin-induced ROS generation by antioxidant N-acetylcysteine resulted in the prevention of G1 arrest and apoptosis in NPC cells. Naringin-induced ROS-mediated G1 arrest and mitochondrial-, death receptor-, and endoplasmic reticulum stress-mediated apoptosis may be a promising strategy for treating NPC.

The changes of intestinal microbiota and metabolomics during the inhibition of bladder cancer by liquiritigenin.

Liquiritigenin is a natural medicine. However, its inhibitory effect and its potential mechanism on bladder cancer (BCa) remain to be explored. It was found that it could be visualized that the transplanted tumours in the low-dose liquiritigenin -treated group and the high-dose liquiritigenin -treated group were smaller than those in the model group. Liquiritigenin treatment led to alterations in Lachnoclostridium, Escherichia-Shigella, Alistipes and Akkermansia. Non-targeted metabolomics analysis showed that a total of multiple differential metabolites were identified between the model group and the high-dose liquiritigenin-treated group. This provides a new direction and rationale for the antitumour effects of liquiritigenin.

The Mitogenomic Landscape of Hexacorallia Corals: Insight into Their Slow Evolution.

The utility of the mitochondrial genomes (mitogenomes) in analyzing the evolutionary history of animals has been proven. Five deep-sea corals (Bathypathes sp.1, Bathypathes sp.2, Schizopathidae 1, Trissopathes sp., and Leiopathes sp.) were collected in the South China Sea (SCS). Initially, the structures and collinearity of the five deep-sea coral mitogenomes were analyzed. The gene arrangements in the five deep-sea coral mitogenomes were similar to those in the order Antipatharia, which evidenced their conservation throughout evolutionary history. Additionally, to elucidate the slow evolutionary rates in Hexacorallia mitogenomes, we conducted comprehensive analyses, including examining phylogenetic relationships, performing average nucleotide identity (ANI) analysis, and assessing GC-skew dissimilarity combining five deep-sea coral mitogenomes and 522 reference Hexacorallia mitogenomes. Phylogenetic analysis using 13 conserved proteins revealed that species clustered together at the order level, and they exhibited interspersed distributions at the family level. The ANI results revealed that species had significant similarities (identity > 85%) within the same order, while species from different orders showed notable differences (identity < 80%). The investigation of the Hexacorallia mitogenomes also highlighted that the GC-skew dissimilarity was highly significant at the order level, but not as pronounced at the family level. These results might be attributed to the slow evolution rate of Hexacorallia mitogenomes and provide evidence of mitogenomic diversity. Furthermore, divergence time analysis revealed older divergence times assessed via mitogenomes compared with nuclear data, shedding light on significant evolutionary events shaping distinct orders within Hexacorallia corals. Those findings provide new insights into understanding the slow evolutionary rates of deep-sea corals in all lineages of Hexacorallia using their mitogenomes.

Identify Regioselective Residues of Ginsenoside Hydrolases by Graph-Based Active Learning from Molecular Dynamics.

Identifying the catalytic regioselectivity of enzymes remains a challenge. Compared to experimental trial-and-error approaches, computational methods like molecular dynamics simulations provide valuable insights into enzyme characteristics. However, the massive data generated by these simulations hinder the extraction of knowledge about enzyme catalytic mechanisms without adequate modeling techniques. Here, we propose a computational framework utilizing graph-based active learning from molecular dynamics to identify the regioselectivity of ginsenoside hydrolases (GHs), which selectively catalyze C6 or C20 positions to obtain rare deglycosylated bioactive compounds from Panax plants. Experimental results reveal that the dynamic-aware graph model can excellently distinguish GH regioselectivity with accuracy as high as 96-98% even when different enzyme-substrate systems exhibit similar dynamic behaviors. The active learning strategy equips our model to work robustly while reducing the reliance on dynamic data, indicating its capacity to mine sufficient knowledge from short multi-replica simulations. Moreover, the model's interpretability identified crucial residues and features associated with regioselectivity. Our findings contribute to the understanding of GH catalytic mechanisms and provide direct assistance for rational design to improve regioselectivity. We presented a general computational framework for modeling enzyme catalytic specificity from simulation data, paving the way for further integration of experimental and computational approaches in enzyme optimization and design.

Parasedimentitalea psychrophila sp. nov., a psychrophilic bacterium isolated from deep-sea sediment.

A novel bacterium, strain QS115T, was isolated from deep-sea sediment collected from the South China Sea at a depth of 1151 m. Phylogenetic analyses based on 16S rRNA gene sequences indicated that QS115T was most closely related to Parasedimentitalea marina W43T, with similarity of 98.21 %. Strain QS115T shared 82.39 % average nucleotide identity, 26.3 % digital DNA-DNA hybridization and 85.32 % average amino acid identity with P. marina W43T. Cells of strain QS115T were Gram-stain-negative, rod-shaped and grew optimally at 10 °C, pH 7.5 and 2 % (w/v) NaCl. The principal fatty acids were summed feature 8 (C18 : 1 ω7c/ω6c), the major respiratory quinone was ubiquinone-10 and predominant polar lipids were diphosphatidylglycerol, phosphatidylethanolamine, glycophospholipid, phosphatidylglycerol and phosphatidylcholine. Polyphasic analyses of physiological and phenotypic characteristics and genomic studies suggested that strain QS115T represents a novel species of the genus Parasedimentitalea, for which the name Parasedimentitalea psychrophila sp. nov. is proposed (type strain QS115T=MCCC 1K04395T=JCM 34219T).

Comparison of continuous pericapsular nerve group (PENG) block versus continuous fascia iliaca compartment block on pain management and quadriceps muscle strength after total hip arthroplasty: a prospective, randomized controlled study.

BACKGROUND: This investigation aimed to evaluate the impact of continuous pericapsular nerve group (PENG) block and continuous fascia iliac compartment block (FICB) on postoperative pain following total hip arthroplasty (THA). METHODS: This prospective, randomized, and controlled trial recruited a cohort of fifty-seven patients with unilateral femoral neck fractures from Xi'an Aerospace General Hospital in northwest China between July 2020 and November 2021. These patients were randomly assigned to two groups: the continuous PENG block group (PENG group, n = 29) and the continuous FICB group (FICB group, n = 28). Under ultrasound guidance, PENG block and FICB procedures were performed prior to spinal anesthesia, utilizing 20 ml of 0.25% ropivacaine for PENG block and 30 ml of 0.25% ropivacaine for FICB. Subsequently, a catheter was inserted. All study participants received a standardized postoperative multimodal analgesic regimen, including intravenous administration of 30 mg Ketorolac tromethamine every eight hours and patient-controlled neural analgesia (PCNA) after surgery. Numerical rating scale (NRS) scores at rest and during exercise were recorded at various time points: prior to block (T0), 30 min post-blockade (T1), and 6 h (T2), 12 h (T3), 24 h (T4), and 48 h (T5) postoperatively. Additional data collected encompassed postoperative quadriceps muscle strength, the time of initial ambulation after surgery, the number of effective PCNA activations, rescue analgesia requirements, and occurrences of adverse events (such as nausea and vomiting, hematoma, infection, catheter detachment, or displacement) within 48 h following surgery. RESULTS: In the PENG group, the resting NRS pain scores exhibited lower values at T1, T4, and T5 than those at T0. Furthermore, exercise NRS pain scores at T1-T5 were lower in the PENG group than in the FICB group. Similarly, during the same postoperative period, the PENG group demonstrated enhanced quadriceps strength on the affected side compared to the FICB group. Additionally, the PENG group displayed earlier postoperative ambulation and reduced occurrences of effective PCNA activations and rescue analgesia requirements compared to the FICB group. CONCLUSION: Continuous PENG block exhibited superior analgesic efficacy after THA compared to continuous FICB, promoting recovery of quadriceps strength on the affected side and facilitating early postoperative ambulation. TRIAL REGISTRATION: This clinical trial was registered in the China Clinical Trials Center ( http://www.chictr.org.cn ) on 20/07/2020, with the registration number ChiCTR2000034821.

Circular RNA circ_0000741/miR-379-5p/TRIM14 signaling axis promotes HDAC inhibitor (SAHA) tolerance in glioblastoma.

BACKGROUND: Histone deacetylase (HDAC) inhibitor-based therapeutic drug tolerance is a major obstacle to glioblastoma (GBM) treatment. Meanwhile, non-coding RNAs have been reported to be involved in the regulation of HDAC inhibitor (SAHA) tolerance in some human tumors. However, the relationship between circular RNAs (circRNAs) and SAHA tolerance is still unknown. Herein, we explored the role and mechanism of circ_0000741 on SAHA tolerance in GBM. METHODS: Circ_0000741, microRNA-379-5p (miR-379-5p), and tripartite motif-containing 14 (TRIM14) level were detected by real-time quantitative polymerase chain reaction (RT-qPCR). (4-5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT), 5-ethynyl-2'-deoxyuridine (EdU), Colony formation, flow cytometry, and transwell assays were used to detect SAHA tolerance, proliferation, apoptosis, and invasion in SAHA-tolerant GBM cells. Western blot analysis of protein levels of E-cadherin, N-cadherin, and TRIM14. After Starbase2.0 analysis, the binding between miR-379-5p and circ_0000741 or TRIM14 was proved using a dual-luciferase reporter. The role of circ_0000741 on drug tolerance was assessed using a xenograft tumor model in vivo. RESULTS: Circ_0000741 and TRIM14 were upregulated, and miR-379-5p was reduced in SAHA-tolerant GBM cells. Furthermore, circ_0000741 absence reduced SAHA tolerance, suppressed proliferation, invasion, and induced apoptosis in SAHA-tolerant GBM cells. Mechanistically, circ_0000741 might affect TRIM14 content via sponging miR-379-5p. Besides, circ_0000741 silencing enhanced the drug sensitivity of GBM in vivo. CONCLUSION: Circ_0000741 might accelerate SAHA tolerance by regulating the miR-379-5p/TRIM14 axis, which provided a promising therapeutic target for GBM treatment.

Polycaprolactone-Modified Biochar Supported Nanoscale Zero-Valent Iron Coupling with Shewanella putrefaciens CN32 for 1,1,1-Trichloroethane Removal from Simulated Groundwater: Synthesis, Optimization, and Mechanism.

1,1,1-Trichloroethane (1,1,1-TCA) is a typical organochloride solvent in groundwater that poses threats to human health and the environment due to its carcinogenesis and bioaccumulation. In this study, a novel composite with nanoscale zero-valent iron (nZVI) supported by polycaprolac-tone (PCL)-modified biochar (nZVI@PBC) was synthesized via solution intercalation and liquid-phase reduction to address the 1,1,1-TCA pollution problem in groundwater. The synergy effect and improvement mechanism of 1,1,1-TCA removal from simulated groundwater in the presence of nZVI@PBC coupling with Shewanella putrefaciens CN32 were investigated. The results were as follows: (1) The composite surface was rough and porous, and PCL and nZVI were loaded uniformly onto the biochar surface as micro-particles and nanoparticles, respectively; (2) the optimal mass ratio of PCL, biochar, and nZVI was 1:7:2, and the optimal composite dosage was 1.0% (w/v); (3) under the optimal conditions, nZVI@PBC + CN32 exhibited excellent removal performance for 1,1,1-TCA, with a removal rate of 82.98% within 360 h, while the maximum removal rate was only 41.44% in the nZVI + CN32 treatment; (4) the abundance of CN32 and the concentration of adsorbed Fe(II) in the nZVI@PBC + CN32 treatment were significantly higher than that in control treatments, while the total organic carbon (TOC) concentration first increased and then decreased during the culture process; (5) the major improvement mechanisms include the nZVI-mediated chemical reductive dechlorination and the CN32-mediated microbial dissimilatory iron reduction. In conclusion, the nZVI@PBC composite coupling with CN32 can be a potential technique to apply for 1,1,1-TCA removal in groundwater.

Polycyclic Aromatic Hydrocarbons in Topsoils Along the Taipu River Banks in the Yangtze River Delta, China: Occurrence, Source and Risk Assessment.

Taipu River is an important transboundary river and drinking water source in the Yangtze River Delta, China. This study collected 15 topsoil samples along the Taipu River banks and subsequently determined the polycyclic aromatic hydrocarbons (PAHs) concentrations, sources, and ecological and health risks. The sum of toxic 15 PAHs concentrations ranged from 83.13 to 28342.53 ng/g, with a mean of 2828.69 ng/g. High molecular weight (HMW) PAHs were the dominant components and Indene (1,2,3, -cd) benzopyrene (InP) accounted for the highest proportion in individuals. The average PAH concentration in residential land was the highest, followed by those in industrial and agricultural land. The PAH concentration was positively related to contents of total carbon, total nitrogen, ammonium nitrogen, and aminopeptidase activity in soils. The mixed combustion of biomass, coal, and petroleum and traffic emissions could be the primary PAH contributors. The total PAHs at over half of sampling points had relatively high risk quotients and incremental lifetime cancer risk (ILCR) values, posing potential or great ecological threats and health risks.

Toxicity effects of disinfection byproduct chloroacetic acid to Microcystis aeruginosa: Cytotoxicity and mechanisms.

Chlorine-based disinfectants are widely used for disinfection in wastewater treatment. The mechanism of the effects of chlorinated disinfection by-products on cyanobacteria was unclear. Herein, the physiological effects of chloroacetic acid (CAA) on Microcystis aeruginosa (M. aeruginosa), including acute toxicity, oxidative stress, apoptosis, production of microcystin-LR (MC-LR), and the microcystin transportation-related gene mcyH transcript abundance have been investigated. CAA exposure resulted in a significant change in the cell ultrastructure, including thylakoid damage, disappearance of nucleoid, production of gas vacuoles, increase in starch granule, accumulation of lipid droplets, and disruption of cytoplasm membranes. Meanwhile, the apoptosis rate of M. aeruginosa increased with CAA concentration. The production of MC-LR was affected by CAA, and the transcript abundance of mcyH decreased. Our results suggested that CAA poses acute toxicity to M. aeruginosa, and it could cause oxidative damage, stimulate MC-LR production, and damage cell ultrastructure. This study may provide information about the minimum concentration of CAA in the water environment, which is safe for aquatic organisms, especially during the global coronavirus disease 2019 pandemic period.

The proatherosclerotic function of BCAT1 in atherosclerosis development of aged-apolipoprotein E-deficient mice.

Atherosclerosis (AS) is an inflammatory vascular disease. Branched-chain amino acid transaminase 1 (BCAT1) has been implicated in inflammatory diseases, while its role in AS is unclear yet. In ApoE-/- mice with a high fat diet (HDF), BCAT1 was highly up-regulated and more pronounced in aged than in young ApoE-/- mice, which was abundantly expressed in macrophages located in AS lesions. The function of BCAT1 in AS was explored using lentivirus-mediated BCAT1 overexpression. ApoE-/- mice fed a HFD with BCAT1 overexpression exhibited the worsening lipid deposition and pathological injury of aortic tissues, accompanied by aggravated hyperlipidemia as proved by increased serum triglyceride, total cholesterol, and low-density lipoprotein-cholesterol levels. Immunohistochemical staining of vascular cell adhesion molecule-1 (VCAM-1), monocyte chemoattractant protein-1 (MCP-1), and CD68 in the aortic root plaque suggested that BCAT1 overexpression could induce monocyte-endothelial cell adhesion and macrophages infiltration, thereby contributing inflammatory response by promoting TNF-α, IL-6, and IL-1ß expression. Further, in vivo experiments, lipid accumulation, and inflammatory response induced by oxidized-LDL in RAW267.4 cells were also intensified or alleviated by BCAT1 overexpression or knockdown. Finally, BCAT1 overexpression aggravated AS development. These adverse effects of BCAT1 on hyperlipidemia, lipid accumulation, foaming cell formation, and inflammation suggested that the modulation of BCAT1 might be a potential approach to prevent AS disease.